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This is the code repository for TensorFlow 2 Reinforcement Learning Cookbook, published by Packt.
Over 50 recipes to help you build, train, and deploy learning agents for real-world applications
With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications.
Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x.
By the end of this TensorFlow book, you'll be able to:
| Build: Deep RL agents from scratch using the all-new and powerful TensorFlow 2.x framework and Keras API |
| Implement: Deep RL algorithms (DQN, A3C, DDPG, PPO, SAC etc.) with minimal lines of code |
| Train: Deep RL agents in simulated environments (gyms) beyond toy-problems and games to perform real-world tasks like cryptocurrency trading, stock trading, tweet/email management and more! |
| Scale: Distributed training of RL agents using TensorFlow 2.x, Ray + Tune + RLLib |
| Deploy: RL agents to the cloud and edge for real-world testing by creating cloud services, web apps and Android mobile apps using TensorFlow Lite, TensorFlow.js, ONNX and Triton |
This book covers the following exciting features:
- Build deep reinforcement learning agents from scratch using the all-new TensorFlow 2.x and Keras API
- Implement state-of-the-art deep reinforcement learning algorithms using minimal code
- Build, train, and package deep RL agents for cryptocurrency and stock trading
- Deploy RL agents to the cloud and edge to test them by creating desktop, web, and mobile apps and cloud services
- Speed up agent development using distributed DNN model training
- Explore distributed deep RL architectures and discover opportunities in AIaaS (AI as a Service)
If you feel this book is for you, get your copy today!
If you use, derive, customize any of the recipes in your work, please cite this book using the following BibTex:
@book{Palanisamy:2021,
author = {Palanisamy, Praveen},
title = {TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications},
year = {2021},
isbn = {183898254X, 9781838982546},
publisher = {Packt Publishing},
}It is advised to create and use a Python virtual environment named tfrl-cookbook to install the packages and run the code in this book. A Miniconda or Anaconda installation for Python virtual environment management is recommended. Follow these steps to set up a conda pyton virtual environment using miniconda:
- Install system dependencies:
- Linux (Ubuntu/Debian):
sudo apt install -y make cmake ffmpeg - Mac OSX:
brew install make cmake ffmpeg
- Linux (Ubuntu/Debian):
- Install miniconda:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && bash -b -p ${HOME}/miniconda3 - Setup conda python virtual environment:
bash && conda env create -f tfrl-cookbook.yml -n "tfrl-cookbook"You are all set! - Activate the
tfrl-cookbookconda python environment:conda activate tfrl-cookbookand get started with the recipes in the book!
It is highly recommended to star and fork the GitHub repository to receive updates and improvements to the code recipes.We urge you to share what you build and also engage with other readers and the community here.
With the following software and hardware list you can run all code files present in the book (Chapter 1-9).
| Chapter | Software required | OS required |
|---|---|---|
| 1 - 9 | Python 3.6 (or later versions) | Windows, Mac OS X, and Linux (Any) |
| 9 | Android Studio | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Praveen Palanisamy works on advancing AI for autonomous systems as a senior AI engineer at Microsoft. In the past, he has developed AI algorithms for autonomous vehicles using deep reinforcement learning, and has worked with start-ups and in academia to build autonomous robots and intelligent systems. He is the inventor of more than 15 patents on learning-based AI systems. He is the author of HOIAWOG: Hands-On Intelligent Agents with OpenAI Gym, which provides a step-by-step guide to developing deep RL agents to solve complex problems from scratch. He has a master's in robotics from Carnegie Mellon University.
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